U.S. patent application number 14/953457 was filed with the patent office on 2017-03-02 for scalable streaming decision tree learning.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Wei Shan Dong, Peng Gao, Guo Qiang Hu, Chang Sheng LI, Xu Liang Li, Chun Yang Ma, Zhi Wang, Xin Zhang.
Application Number | 20170061327 14/953457 |
Document ID | / |
Family ID | 58096757 |
Filed Date | 2017-03-02 |
United States Patent
Application |
20170061327 |
Kind Code |
A1 |
Dong; Wei Shan ; et
al. |
March 2, 2017 |
SCALABLE STREAMING DECISION TREE LEARNING
Abstract
In one embodiment, a computer-implemented method includes
receiving training data including a plurality of records, each
record having a plurality of attributes. The training data is
horizontally parallelized across two or more processing elements.
This horizontal parallelizing includes dividing the training data
into two or more subsets of records; assigning each subset of
records to a corresponding processing element of the two or more
processing elements; transmitting each subset of records to its
assigned processing element; and sorting, at the two or more
processing elements, the two or more subsets of records to two or
more candidate leaves of a decision tree. The output from
horizontally parallelizing is converted into input for vertically
parallelizing the training data. The training data is vertically
parallelized across the two or more processing elements. The
decision tree is grown based at least in part on the horizontally
parallelizing, the converting, and the vertically
parallelizing.
Inventors: |
Dong; Wei Shan; (Beijing,
CN) ; Gao; Peng; (Beijing, CN) ; Hu; Guo
Qiang; (Shanghai, CN) ; LI; Chang Sheng;
(Beijing, CN) ; Li; Xu Liang; (Beijimg, CN)
; Ma; Chun Yang; (Beijing, CN) ; Wang; Zhi;
(Beijing, CN) ; Zhang; Xin; (Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
58096757 |
Appl. No.: |
14/953457 |
Filed: |
November 30, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14833397 |
Aug 24, 2015 |
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14953457 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 5/025 20130101;
G06N 5/02 20130101 |
International
Class: |
G06N 99/00 20060101
G06N099/00; G06N 5/02 20060101 G06N005/02 |
Claims
1. A computer-implemented method, comprising: receiving, with a
processing device, training data comprising a plurality of records,
each of the plurality of records comprising a plurality of
attributes; horizontally parallelizing the training data across two
or more processing elements, wherein the horizontally parallelizing
the training data comprises: dividing the training data into two or
more subsets of records; assigning each subset of records to a
corresponding processing element of the two or more processing
elements; transmitting each subset of records to its assigned
processing element; and sorting, at the two or more processing
elements, the two or more subsets of records to two or more
candidate leaves of a decision tree; converting output from the
horizontally parallelizing into input for vertically parallelizing
the training data; vertically parallelizing the training data
across the two or more processing elements, wherein the vertically
parallelizing the training data comprises: dividing the training
data into two or more subsets of attributes; assigning each subset
of attributes to a corresponding processing element of the two or
more processing elements; and transmitting each subset of
attributes of the plurality of records to the assigned processing
element for the subset of attributes; and growing the decision tree
based at least in part on the horizontally parallelizing, the
converting, and the vertically parallelizing.
2. The method of claim 1, wherein the converting the output from
the horizontally parallelizing into the input for vertically
parallelizing the training data comprises: dividing the two or more
candidate leaves of the decision tree into two or more subsets of
candidate leaves; and assigning each of the two or more subsets of
candidate leaves to a corresponding processing element of the two
or more processing elements.
3. The method of claim 1, wherein the converting the output from
the horizontally parallelizing into the input for vertically
parallelizing the training data comprises: receiving statistics
related to the two or more candidate leaves to which the two or
more subsets of records were sorted; and aggregating the statistics
for each of the two or more candidate leaves.
4. The method of claim 1, wherein the vertically parallelizing the
training data further comprises: calculating, at each of the two or
more processing elements, an information gain of splitting a first
candidate leaf on each of the attributes in the subset of
attributes assigned to the processing element.
5. The method of claim 4, further comprising: aggregating results
of the calculating the information gain; and determining how to
split each of the two or more candidate leaves of the decision
tree, based at least in part on the aggregated results.
6. The method of claim 5, wherein the growing the decision tree
based at least in part on the horizontally parallelizing, the
converting, and the vertically parallelizing comprises updating the
decision tree based on the determining how to split each of the two
or more candidate leaves.
7. The method of claim 1, further comprising repeating the
horizontally parallelizing, the converting, and the vertically
parallelizing until the decision tree is complete.
Description
DOMESTIC PRIORITY
[0001] This application is a continuation of U.S. patent
application Ser. No.: 14/833,397 filed Aug. 24, 2015, and all the
benefits accruing therefrom under 35 U.S.C. .sctn.119, the contents
of which is herein incorporated by reference in its entirety.
BACKGROUND
[0002] Various embodiments of this disclosure relate to decision
trees and, more particularly, to scalable streaming decision tree
learning.
[0003] Many applications require the processing of Big data, which
may be at rest or in motion. Big data is a broad term for data sets
so large or complex that traditional data processing applications
are inadequate. Challenges include analysis, capture, data
curation, search, sharing, storage, transfer, visualization, and
information privacy. When the data is in motion, processing it may
need to take place in real time. One mechanism for processing data
is through the use of decision trees.
[0004] When a decision tree is used, data can be classified by
stepping through the nodes of the tree based on known attributes of
the data. At each node, a child node is selected based on the value
of an attribute of the data, and this selection process may
continue until a leaf of the tree is selected. The leaf may be
associated with a value to be assigned as a classification value
for the data.
[0005] Decision tree learning is a form of classification learning,
used to determine how to classify data. In decision tree learning,
a system generates a decision tree that will be used to classify
data based on observed attributes. Through the generation of the
decision tree, which is an iterative generation, each interior node
is split into subsets, with a child node at the root of each
subset, based on the value of an attribute associated with that
interior node. Each edge leading from the interior node to a child
node corresponds to a particular value of the attribute. If no more
attributes are available, a node then becomes a leaf node in the
final decision tree, corresponding to a classification or
prediction of a final value for the data.
[0006] In some systems, parallelism is used to speed up decision
tree learning where a large amount of training data is being used
as input into generating the decision tree. Specifically, either
horizontal of vertical parallelism is used. The data generally
includes multiple records, with each record containing multiple
attributes, or columns. With vertical parallelism, the set of
attributes are divided among available processing elements. In
other words, each processing element receives data from multiple
records, but the data received by each processing element includes
only a subset of the existing attributes. With horizontal
parallelism, the set of records are divided among available
processing elements. In this case, each processing element receives
data from one or more records, including every attribute for the
subset of records assigned to that processing element.
[0007] Each processing element operates on the data assigned to it.
The results of these operations are then aggregated together to
complete generation of the decision tree.
SUMMARY
[0008] In one embodiment of this disclosure, a computer-implemented
method includes receiving training data including a plurality of
records, where each of the plurality of records has a plurality of
attributes. The training data is horizontally parallelized across
two or more processing elements. This horizontal parallelizing
includes dividing the training data into two or more subsets of
records; assigning each subset of records to a corresponding
processing element of the two or more processing elements;
transmitting each subset of records to its assigned processing
element; and sorting, at the two or more processing elements, the
two or more subsets of records to two or more candidate leaves of a
decision tree. The output from the horizontally parallelizing is
converted into input for vertically parallelizing the training
data. The training data is vertically parallelized across the two
or more processing elements. This vertical parallelizing includes
dividing the training data into two or more subsets of attributes;
assigning each subset of attributes to a corresponding processing
element of the two or more processing elements; and transmitting
each subset of attributes of the plurality of records to the
assigned processing element for the subset of attributes. The
decision tree is grown based at least in part on the horizontally
parallelizing, the converting, and the vertically
parallelizing.
[0009] In another embodiment, a system includes a memory and one or
more computer processors communicatively coupled to the memory. The
one or more computer processors are configured to receive training
data including a plurality of records, each of the plurality of
records having a plurality of attributes. The one or more computer
processors are further configured to horizontally parallelize the
training data across two or more processing elements. To
horizontally parallelize the training data, the one or more
computer processors are further configured to divide the training
data into two or more subsets of records; assign each subset of
records to a corresponding processing element of the two or more
processing elements; transmit each subset of records to its
assigned processing element; and sort, at the two or more
processing elements, the two or more subsets of records to two or
more candidate leaves of a decision tree. The one or more computer
processors are further configured to convert output from the
horizontally parallelizing into input for vertically parallelizing
the training data. The one or more computer processors are further
configured to vertically parallelize the training data across the
two or more processing elements. To vertically parallelize the
training data, the one or more computer processors are further
configured to divide the training data into two or more subsets of
attributes; assign each subset of attributes to a corresponding
processing element of the two or more processing elements; and
transmit each subset of attributes of the plurality of records to
the assigned processing element for the subset of attributes. The
one or more computer processors are further configured to grow the
decision tree based at least in part on the horizontally
parallelizing, the converting, and the vertically
parallelizing.
[0010] In yet another embodiment, a computer program product for
generating a decision tree includes a computer readable storage
medium having program instructions embodied therewith. The program
instructions are executable by a processor to cause the processor
to perform a method. The method includes receiving training data
including a plurality of records, where each of the plurality of
records has a plurality of attributes. Further according to the
method, the training data is horizontally parallelized across two
or more processing elements. This horizontal parallelizing includes
dividing the training data into two or more subsets of records;
assigning each subset of records to a corresponding processing
element of the two or more processing elements; transmitting each
subset of records to its assigned processing element; and sorting,
at the two or more processing elements, the two or more subsets of
records to two or more candidate leaves of a decision tree. The
output from the horizontally parallelizing is converted into input
for vertically parallelizing the training data. The training data
is vertically parallelized across the two or more processing
elements. This vertical parallelizing includes dividing the
training data into two or more subsets of attributes; assigning
each subset of attributes to a corresponding processing element of
the two or more processing elements; and transmitting each subset
of attributes of the plurality of records to the assigned
processing element for the subset of attributes. The decision tree
is grown based at least in part on the horizontally parallelizing,
the converting, and the vertically parallelizing.
[0011] Additional features and advantages are realized through the
techniques of the present invention. Other embodiments and aspects
of the invention are described in detail herein and are considered
a part of the claimed invention. For a better understanding of the
invention with the advantages and the features, refer to the
description and to the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The subject matter which is regarded as the invention is
particularly pointed out and distinctly claimed in the claims at
the conclusion of the specification. The forgoing and other
features, and advantages of the invention are apparent from the
following detailed description taken in conjunction with the
accompanying drawings in which:
[0013] FIG. 1 is a diagram of an example decision tree upon which a
tree-growing system may operate, according to some embodiments of
this disclosure;
[0014] FIG. 2 is a block diagram of the tree-growing system,
according to some embodiments of this disclosure;
[0015] FIG. 3 is a flow diagram of a method for growing the
decision tree, according to some embodiments of this disclosure;
and
[0016] FIG. 4 is a block diagram of a computer system for
implementing some or all aspects of the tree-growing system,
according to some embodiments of this disclosure.
DETAILED DESCRIPTION
[0017] Various embodiments of this disclosure are configured to
grow, also referred to as learning or generating, decision trees
based on training data. Generally, vertical parallelism works well
for this purpose when the dimension of data is high (i.e., when
there are many attributes), and horizontal parallelism works well
when the rate at which the data arrives is high. However,
conventional mechanisms are not scalable to work well for
situations in which data arrives at high velocity and has a high
dimension. Various embodiments of this disclosure address such
situations.
[0018] FIG. 1 is a diagram of an example decision tree 110 upon
which a tree-growing system 100, according to some embodiments
herein, may operate. In this example, the tree-growing system 100
has already begun growing the decision tree 110. Each node 120 of
the decision tree 110 may represent an operator. Specifically, the
operator of a node 120 may determine which edge an item of data
takes from that node 120 to a child node 120 by examining a
particular attribute of the item. In other words, each operator
examines a single attribute, and that attribute can be referred to
as the attribute upon which the associated node 120 is split. Each
value of the attribute may lead to a distinct child node 120
connected to the node 120 in question by a single edge.
[0019] When growing the decision tree 110, the tree-growing system
100 may examine each leaf node 120, or leaf, of the decision tree
110. For each leaf, the tree-growing system 100 may decide whether
to split that leaf and, if so, on which attribute to split the
leaf.
[0020] FIG. 2 is a block diagram of the tree-growing system 100,
according to some embodiments of this disclosure. As shown the,
tree-growing system 100 may have one or more processing layers 210.
At each layer, two or more processing elements 220 may perform
activities in processing training data, so as determine how to grow
the decision tree 110. As a result, the training data may be
parallelized, or distributed, across the processing elements 220
within a processing layer 210 for growing the decision tree 110. In
some embodiments, the processing layers 210 need not be distinct
from one another; rather, the processing elements 220 in one
processing layer 210 may be the same processing elements 210 as in
another processing layer 210. However, the processing layers 210
may represent distinct layers, or sets of operations, in processing
the training data. The training data may be re-parallelized when
passing between the various layers 210.
[0021] FIG. 3 is a flow diagram of a method 300 for growing a
decision tree 110, according to some embodiments of this
disclosure. This method 300 may be performed by the tree-growing
system 100.
[0022] At block 310, the tree-growing system 100 may horizontally
parallelize a set of training data. More specifically, the
tree-growing system 100 may divide the training data into sets of
records, which may be non-overlapping sets. Each set of records may
be directed to its associated processing element 220 for
processing. Each processing element 220 may receive its set of
records, which may be a subset of the complete training data.
[0023] At block 320, within a first processing layer 210a, referred
to as the instance layer of the tree-growing system 100, each
processing element 220 may sort each of its records to a leaf of
the decision tree 110. The leaves to which records are sorted may
then become candidate leaves for splitting in growing the decision
tree. In other words, each candidate leaf may have the potential to
be split in the current pass through the processing layers 210.
[0024] After sorting the records to the applicable candidate
leaves, at block 325, each processing elements 220 may update the
statistics associated with the candidate leaves based on the
records sorted to them. Each leaf of the decision tree 110 may be
associated with statistics related to data records sorted to that
leaf. Those statistics may include, for example, a count of how
many records have been sorted to that leaf. Thus, in response to a
record being sorted to a leaf, the statistics associated with that
leaf may be updated, which may include incrementing the count
associated with the leaf. In some embodiments, each processing
element 220 may have access to its own replica of the decision tree
110. Thus, this updating of statistics may be performed locally by
each processing element 220, on only the local replica of the
decision tree 110.
[0025] At block 330, the candidate leaves may be grouped into sets
of leaves, with each set being associated with a particular
processing element 220 that is responsible for that leaf. In some
embodiments, the sets of leaves may be non-overlapping, such that a
leaf associated with a first processing element 220 is associated
with no other processing element. At this block, for each candidate
leaf, the statistical information for that candidate leaf in the
various decision tree replicas may be transmitted to the processing
element 220 responsible for that leaf. In some cases, one or more
records from across the various processing elements 220 may have
been sorted to the same leaf, and in that case, the updated
statistical information related to those one or more records may
then be directed to the same processing element 220 at block
330.
[0026] At this point, each processing element 220 may receive
updated statistical data from the other processing elements 220 for
each candidate leaf for which the processing element is
responsible. At block 335, within a second layer 210b, referred to
as the leaf layer of the tree-growing system 100, each processing
element 220 may aggregate the statistical data for each candidate
leaf for which the processing element 220 is responsible. In other
words, for example, statistical data that was updated based on
sorting a first record to a first leaf at a first processing
element 220 may be aggregated with other statistical data that was
updated based on sorting other records to the first leaf at other
processing elements 220. Thus, each processing element 220 may have
aggregated data for each candidate leaf for which it is
responsible.
[0027] At block 340, each processing element 220 may transmit the
updated statistical information for each of its assigned leaves to
the other processing elements 220, such that each processing
element 220 then has the updated and aggregated statistical data
for all candidate leaves.
[0028] The tree-growing system 100 may then vertically parallelize
the processing of training data across the processing elements.
Specifically, at block 340, each processing element may be assigned
a set of attributes, with each set of attributes being a subset of
all the attributes in the training data. Further, the sets of
attributes may be non-overlapping, such that a particular attribute
is assigned to no more than a single processing element 220.
[0029] Thus, given that the training data is horizontally
parallelized in the instance layer 210a and vertically parallelized
in an attribute later 210c, which is discussed below, the
intermediate leaf layer 210b may receive as input horizontally
parallelized data and may convert that data into vertically
parallelized data. Due to the leaf layer 210b, the tree-growing
system 100 may enable both horizontal and vertical parallelization
when determining whether and how to split candidate leaves. The
tree-growing system 100 may thus be scalable both vertically and
horizontally.
[0030] At block 350, within a third layer 210c, referred to as the
attribute layer of the tree-growing system 100, each processing
element 220 may calculate an information gain of splitting each
candidate leaf at the attributes assigned to that processing
element 220. Given a current state of the decision tree 110, and
given a current candidate leaf, there may exist an information gain
related to each attribute. The information gain associated with an
attribute may represent the benefit of splitting the leaf on that
attribute. Various existing mechanisms may be used for calculating
the information gain of an attribute for the purpose of growing a
decision tree, and these existing mechanisms may be used in
implementing the present tree-growing system 100.
[0031] At block 355, the result of the calculations of information
gains may be shared with all other processing elements 220. In
other words, each processing element 220 may transmit the result of
its information gain calculations to the other processing elements
220.
[0032] The candidate leaves may be once again divided into sets of
leaves, with each set being associated with a particular processing
element 220 that is responsible for that leaf. In some embodiments,
the sets may be non-overlapping, such that a leaf associated with a
first processing element 220 is associated with no other processing
element. At block 365, within a fourth layer 210d, referred to as
the update layer of the tree-growing system 100, for each candidate
leaf for which a processing element 220 is responsible, that
processing element 220 may determine whether and on which attribute
to split that leaf. Specifically, the processing element 220 may
aggregate all the information gain values associated with the
various attributes for the leaf in question. The processing element
220 may then split the leaf according to the decision tree's
growing criteria, which are the criteria for selecting which
attribute on which to split a leaf, given the information gains
associated with the various attributes for that leaf. Various
growing criteria exist in the art, based on information gain
values, and such criteria may be used with embodiments of the
present tree-growing system 100.
[0033] At block 370, the decision tree 110 may be updated by
splitting the decision tree 110 at zero or more candidate leaves
based on the decisions made by the various processing elements 220
in block 365. At decision block 375, it may be determined whether
the decision tree 110 is complete. In some embodiments, the
decision tree 110 may be considered complete if no candidate leaves
were split in the pass through the processing layers 210. If the
decision tree 110 is complete, then the method 300 may end at block
380. Alternatively, if the decision tree 110 is not complete, then
the method 300 may return to block 310 to continue growing the
decision tree 110.
[0034] FIG. 4 illustrates a block diagram of a computer system 400
for use in implementing a tree-growing system or method according
to some embodiments. The tree-growing systems and methods described
herein may be implemented in hardware, software (e.g., firmware),
or a combination thereof. In some embodiments, the methods
described may be implemented, at least in part, in hardware and may
be part of the microprocessor of a special or general-purpose
computer system 400, such as a personal computer, workstation,
minicomputer, or mainframe computer. For example, and not by way of
limitation, each processing element 220 may be a processor 405, or
core of a processor 405, of the computer system 400.
[0035] In some embodiments, as shown in FIG. 4, the computer system
400 includes a processor 405, memory 410 coupled to a memory
controller 415, and one or more input devices 445 and/or output
devices 440, such as peripherals, that are communicatively coupled
via a local I/O controller 435. These devices 440 and 445 may
include, for example, a printer, a scanner, a microphone, and the
like. Input devices such as a conventional keyboard 450 and mouse
455 may be coupled to the I/O controller 435. The I/O controller
435 may be, for example, one or more buses or other wired or
wireless connections, as are known in the art. The I/O controller
435 may have additional elements, which are omitted for simplicity,
such as controllers, buffers (caches), drivers, repeaters, and
receivers, to enable communications.
[0036] The I/O devices 440, 445 may further include devices that
communicate both inputs and outputs, for instance disk and tape
storage, a network interface card (NIC) or modulator/demodulator
(for accessing other files, devices, systems, or a network), a
radio frequency (RF) or other transceiver, a telephonic interface,
a bridge, a router, and the like.
[0037] The processor 405 is a hardware device for executing
hardware instructions or software, particularly those stored in
memory 410. The processor 405 may be a custom made or commercially
available processor, a central processing unit (CPU), an auxiliary
processor among several processors associated with the computer
system 400, a semiconductor based microprocessor (in the form of a
microchip or chip set), a macroprocessor, or other device for
executing instructions. The processor 405 includes a cache 470,
which may include, but is not limited to, an instruction cache to
speed up executable instruction fetch, a data cache to speed up
data fetch and store, and a translation lookaside buffer (TLB) used
to speed up virtual-to-physical address translation for both
executable instructions and data. The cache 470 may be organized as
a hierarchy of more cache levels (L1, L2, etc.).
[0038] The memory 410 may include one or combinations of volatile
memory elements (e.g., random access memory, RAM, such as DRAM,
SRAM, SDRAM, etc.) and nonvolatile memory elements (e.g., ROM,
erasable programmable read only memory (EPROM), electronically
erasable programmable read only memory (EEPROM), programmable read
only memory (PROM), tape, compact disc read only memory (CD-ROM),
disk, diskette, cartridge, cassette or the like, etc.). Moreover,
the memory 410 may incorporate electronic, magnetic, optical, or
other types of storage media. Note that the memory 410 may have a
distributed architecture, where various components are situated
remote from one another but may be accessed by the processor
405.
[0039] The instructions in memory 410 may include one or more
separate programs, each of which comprises an ordered listing of
executable instructions for implementing logical functions. In the
example of FIG. 4, the instructions in the memory 410 include a
suitable operating system (OS) 411. The operating system 411
essentially may control the execution of other computer programs
and provides scheduling, input-output control, file and data
management, memory management, and communication control and
related services.
[0040] Additional data, including, for example, instructions for
the processor 405 or other retrievable information, may be stored
in storage 420, which may be a storage device such as a hard disk
drive or solid state drive. The stored instructions in memory 410
or in storage 420 may include those enabling the processor to
execute one or more aspects of the tree-growing systems and methods
of this disclosure.
[0041] The computer system 400 may further include a display
controller 425 coupled to a display 430. In some embodiments, the
computer system 400 may further include a network interface 460 for
coupling to a network 465. The network 465 may be an IP-based
network for communication between the computer system 400 and an
external server, client and the like via a broadband connection.
The network 465 transmits and receives data between the computer
system 400 and external systems. In some embodiments, the network
465 may be a managed IP network administered by a service provider.
The network 465 may be implemented in a wireless fashion, e.g.,
using wireless protocols and technologies, such as WiFi, WiMax,
etc. The network 465 may also be a packet-switched network such as
a local area network, wide area network, metropolitan area network,
the Internet, or other similar type of network environment. The
network 465 may be a fixed wireless network, a wireless local area
network (LAN), a wireless wide area network (WAN) a personal area
network (PAN), a virtual private network (VPN), intranet or other
suitable network system and may include equipment for receiving and
transmitting signals.
[0042] Tree-growing systems and methods according to this
disclosure may be embodied, in whole or in part, in computer
program products or in computer systems 400, such as that
illustrated in FIG. 4.
[0043] Technical effects and benefits of some embodiments include
the ability to further parallelize decision tree learning, so as to
better leverage the parallelization capacity of a computer system
100. Some embodiments may use both vertical and horizontal
parallelization to more efficiently grow a decision tree, as
compared to conventional systems.
[0044] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
[0045] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements in the
claims below are intended to include any structure, material, or
act for performing the function in combination with other claimed
elements as specifically claimed. The description of the present
invention has been presented for purposes of illustration and
description, but is not intended to be exhaustive or limited to the
invention in the form disclosed. Many modifications and variations
will be apparent to those of ordinary skill in the art without
departing from the scope and spirit of the invention. The
embodiments were chosen and described in order to best explain the
principles of the invention and the practical application, and to
enable others of ordinary skill in the art to understand the
invention for various embodiments with various modifications as are
suited to the particular use contemplated.
[0046] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0047] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0048] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0049] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Java, Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0050] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0051] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0052] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0053] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0054] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
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